Results 61 to 70 of about 44,986 (305)
Applied SciencesBayesian model updating has received considerable attention and has been extensively used in structural damage detection. It provides a rigorous statistical framework for realizing structural system identification and characterizing uncertainties ...
Luling Liu +3 more
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Thin-sheet electromagnetic inversion modeling using Monte Carlo Markov Chain (MCMC) algorithm [PDF]
Earth, Planets and Space, 2014 The well-known thin-sheet modeling has become a very useful interpretation tool in electromagnetic (EM) methods. The thin-sheet model approximates fairly well 3-D heterogeneities having a limited vertical dimension. This type of approximation leads to amenable computation of EM response of a relatively complex conductivity distribution.
Grandis, Hendra +2 more
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JCPP Advances, EarlyView.Abstract Background Adolescence is marked by increased vulnerability to sleep disturbances and mood disorders. Understanding how day‐to‐day changes in sleep and mood are linked within the same individual is crucial for clarifying sleep's role in emerging internalizing disorders. However, the extent to which an adolescent's fluctuations in sleep predict
Konstantin Drexl +4 more
wiley +1 more source
Journal of Traumatic Stress, EarlyView.Abstract Adverse and benevolent childhood experiences (ACEs and BCEs, respectively) are uniquely associated with posttraumatic stress disorder (PTSD) and complex PTSD (CPTSD); however, there is no systematic review on the mechanisms of these associations.
Sarah Louise Guthrie +4 more
wiley +1 more source
IMPLEMENTASI METODE MARKOV CHAIN MONTE CARLO DALAM PENENTUAN HARGA KONTRAK BERJANGKA KOMODITAS
E-Jurnal Matematika, 2015 The aim of the research is to implement Markov Chain Monte Carlo (MCMC) simulation method to price the futures contract of cocoa commodities. The result shows that MCMC is more flexible than Standard Monte Carlo (SMC) simulation method because MCMC ...
PUTU AMANDA SETIAWANI +2 more
doaj
Interfailure Data with Constant Hazard Function in the Presence of Change-Points
Revstat Statistical Journal, 2007 Markov Chain Monte Carlo (MCMC) methods are used to perform a Bayesian analysis for interfailure data with constant hazard function in the presence of one or more change-points.
Jorge Alberto Achcar +2 more
doaj +1 more source
Rainfall Frequency Analysis of Sudan by Using Bayesian Markov chain Monte Carlo (MCMC) methods [PDF]
Proceedings of the 2013 International Conference on Information Science and Technology Applications, 2013 (10) and it is commonly used to implement a regional frequency analysis for a particular variable of interest. The aim of regional frequency analysis is to increase the information content of the analysis and to reduce the uncertainty of the design values estimates by 'trading space for time'.
Ping Feng +2 more
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The Journal of Wildlife Management, EarlyView.We used 4 sampling methods to estimate or index the abundance and sex ratio of spotted salamanders (Ambystoma maculatum) over 14 years. The present study highlights the importance of considering individual heterogeneity in capture probability when estimating abundance of pond‐breeding amphibians from capture data with imperfect detection. Abstract Long‐
Patrick D. Moldowan +3 more
wiley +1 more source
Introduction to Monte Carlo Markov Chains (MCMC)
, 2017 This lecture provides a step by step explanation of the purpose and process of parameter estimation using MCMC integration. The lecture contains a series of videos to break down the algorithm and also shows how tuning MCMC samplers can affect convergence.
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CLTs and asymptotic variance of time-sampled Markov chains [PDF]
, 2011 For a Markov transition kernel P and a probability distribution μ on nonnegative integers, a time-sampled Markov chain evolves according to the transition kernel Pμ = Σkμ(k)Pk.
Łatuszyński, Krzysztof +3 more
core +1 more source